Demands and Needs for Psycho-Oncological eHealth Interventions in Women With Cancer: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Over the last decade, a growing body of studies regarding the application of eHealth and various digital interventions has been published and are widely used in the psycho-oncological care. However, the effectiveness of eHealth applications in psycho-oncological care is still questioned due to missing considerations regarding evidence-based studies on the demands and needs in cancer-affected patients. OBJECTIVE: This cross-sectional study aimed to explore the cancer-affected women's needs and wishes for psycho-oncological content topics in eHealth applications and whether women with cancer differ in their content topics and eHealth preferences regarding their experienced psychological burden. METHODS: Patients were recruited via an electronic online survey through social media, special patient Internet platforms, and patient networks (both inpatients and outpatients, University Hospital Tuebingen, Germany). Participant demographics, preferences for eHealth and psycho-oncological content topics, and their experienced psychological burden of distress, quality of life, and need for psychosocial support were evaluated. RESULTS: Of the 1172 patients who responded, 716 were included in the study. The highest preference for psycho-oncological content topics reached anxiety, ability to cope, quality of life, depressive feelings, and adjustment toward a new life situation. eHealth applications such as Web-based applications, websites, blogs, info email, and consultation hotline were considered to be suitable to convey these content topics. Psychological burden did not influence the preference rates according to psycho-oncological content and eHealth applications. CONCLUSIONS: Psycho-oncological eHealth applications may be very beneficial for women with cancer, especially when they address psycho-oncological content topics like anxiety, ability to cope, depressive feelings, self-esteem, or adjustment to a new life situation. The findings of this study indicate that psycho-oncological eHealth applications are a promising medium to improve the psychosocial care and enhance individual disease management and engagement among women with cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".